{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reduce_mem_usage_colwise(col):\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    col_type = col.dtypes\n",
    "    if col_type in numerics:\n",
    "        c_min = col.min()\n",
    "        c_max = col.max()\n",
    "        if str(col_type)[:3] == 'int':\n",
    "            if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                col = col.astype(np.int8)\n",
    "            elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                col = col.astype(np.int16)\n",
    "            elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                col = col.astype(np.int32)\n",
    "            elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                col = col.astype(np.int64)  \n",
    "        else:\n",
    "            if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                col = col.astype(np.float16)\n",
    "            elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                col = col.astype(np.float32)\n",
    "            else:\n",
    "                col = col.astype(np.float64)       \n",
    "    return col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "import multiprocessing as mp\n",
    "from functools import partial\n",
    "import gc\n",
    "import time\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.2 s, sys: 1.92 s, total: 21.1 s\n",
      "Wall time: 21.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "DATA_PATH = 'Data/'\n",
    "OUTPUT_PATH = 'Output/'\n",
    "\n",
    "oof_prev = pd.read_csv(OUTPUT_PATH + 'oofs_lgbm.csv')\n",
    "preds_prev = pd.read_excel(OUTPUT_PATH + 'preds_lgbm.xlsx')\n",
    "\n",
    "train = pd.read_csv(DATA_PATH + 'train_data.csv')\n",
    "train_labels = pd.read_csv(DATA_PATH + 'train_labels.csv')\n",
    "test = pd.read_csv(DATA_PATH + 'test_data.csv')\n",
    "sample_sub = pd.read_csv(DATA_PATH + 'Sample Submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.merge(train, train_labels, on = 'patient_id', how='left')\n",
    "df = pd.concat([train, test])\n",
    "df = df.drop_duplicates()\n",
    "comp_train = train_labels.copy()\n",
    "comp_test = sample_sub.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[df['event_time'] < 180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['made_payment'] = pd.qcut(df['patient_payment'], 20000, duplicates='drop', labels=False).astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.3 s, sys: 960 ms, total: 12.2 s\n",
      "Wall time: 12.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for c in ['event_name', 'plan_type', 'specialty', 'made_payment']:\n",
    "    recency = df.groupby(c).apply(lambda x: x.groupby('patient_id')['event_time'].min()).unstack().T.reset_index()\n",
    "    recency.columns = [col + '_' + c if col != 'patient_id' else 'patient_id' for col in recency.columns]\n",
    "    comp_train = pd.merge(comp_train, recency, on = 'patient_id', how='left')\n",
    "    comp_test = pd.merge(comp_test, recency, on = 'patient_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comp_train['prev_preds'] = oof_prev['outcome_flag']\n",
    "comp_test['prev_preds'] = preds_prev['outcome_flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comp_train = comp_train.apply(lambda x: reduce_mem_usage_colwise(x))\n",
    "comp_test = comp_test.apply(lambda x: reduce_mem_usage_colwise(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14350"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del train, test, df, recency, oof_prev, preds_prev, train_labels, sample_sub\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2106\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2107"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_COL, TARGET_COL = 'patient_id', 'outcome_flag'\n",
    "features = [c for c in comp_train if c not in [ID_COL, TARGET_COL]]\n",
    "target = comp_train[TARGET_COL]\n",
    "features = comp_train[features].isnull().sum().sort_values(ascending=False)[400:].index.tolist()\n",
    "print(len(features))\n",
    "to_drop_cols = [c for c in comp_train.columns if c not in features]\n",
    "to_drop_cols.remove(ID_COL)\n",
    "comp_train.drop(to_drop_cols, axis=1, inplace=True)\n",
    "comp_test.drop(to_drop_cols, axis=1, inplace=True)\n",
    "comp_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param = {'num_leaves': 5000,\n",
    "         'min_data_in_leaf': 220, \n",
    "         'objective':'binary',\n",
    "         'max_depth': -1,\n",
    "         'learning_rate': 0.005,\n",
    "         \"boosting\": \"gbdt\",\n",
    "         \"feature_fraction\": 0.22,\n",
    "         \"metric\": 'auc',\n",
    "         \"lambda_l1\": 2,\n",
    "         \"lambda_l2\": 0.6,\n",
    "         \"random_state\": 6,\n",
    "         \"verbosity\": -1,\n",
    "         'scale_pos_weight': 1.15}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold n°0\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.879698\tvalid_1's auc: 0.82273\n",
      "[800]\ttraining's auc: 0.907136\tvalid_1's auc: 0.835588\n",
      "[1200]\ttraining's auc: 0.927749\tvalid_1's auc: 0.842616\n",
      "[1600]\ttraining's auc: 0.94402\tvalid_1's auc: 0.845841\n",
      "[2000]\ttraining's auc: 0.9572\tvalid_1's auc: 0.847337\n",
      "[2400]\ttraining's auc: 0.967631\tvalid_1's auc: 0.847716\n",
      "[2800]\ttraining's auc: 0.975899\tvalid_1's auc: 0.846658\n",
      "[3200]\ttraining's auc: 0.982191\tvalid_1's auc: 0.845875\n",
      "Early stopping, best iteration is:\n",
      "[2493]\ttraining's auc: 0.96976\tvalid_1's auc: 0.847925\n",
      "fold n°1\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.879922\tvalid_1's auc: 0.839042\n",
      "[800]\ttraining's auc: 0.906276\tvalid_1's auc: 0.848395\n",
      "[1200]\ttraining's auc: 0.926625\tvalid_1's auc: 0.853153\n",
      "[1600]\ttraining's auc: 0.943314\tvalid_1's auc: 0.855595\n",
      "[2000]\ttraining's auc: 0.956415\tvalid_1's auc: 0.857289\n",
      "[2400]\ttraining's auc: 0.966624\tvalid_1's auc: 0.859026\n",
      "[2800]\ttraining's auc: 0.974857\tvalid_1's auc: 0.860018\n",
      "[3200]\ttraining's auc: 0.981168\tvalid_1's auc: 0.860709\n",
      "[3600]\ttraining's auc: 0.986098\tvalid_1's auc: 0.861164\n",
      "[4000]\ttraining's auc: 0.989809\tvalid_1's auc: 0.861511\n",
      "[4400]\ttraining's auc: 0.992598\tvalid_1's auc: 0.861958\n",
      "[4800]\ttraining's auc: 0.99459\tvalid_1's auc: 0.862351\n",
      "[5200]\ttraining's auc: 0.996087\tvalid_1's auc: 0.86205\n",
      "Early stopping, best iteration is:\n",
      "[4723]\ttraining's auc: 0.99425\tvalid_1's auc: 0.86242\n",
      "fold n°2\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.879769\tvalid_1's auc: 0.823398\n",
      "[800]\ttraining's auc: 0.906636\tvalid_1's auc: 0.835643\n",
      "[1200]\ttraining's auc: 0.927601\tvalid_1's auc: 0.84184\n",
      "[1600]\ttraining's auc: 0.944315\tvalid_1's auc: 0.846036\n",
      "[2000]\ttraining's auc: 0.957421\tvalid_1's auc: 0.848054\n",
      "[2400]\ttraining's auc: 0.967869\tvalid_1's auc: 0.849951\n",
      "[2800]\ttraining's auc: 0.976073\tvalid_1's auc: 0.850568\n",
      "[3200]\ttraining's auc: 0.982157\tvalid_1's auc: 0.850886\n",
      "[3600]\ttraining's auc: 0.986849\tvalid_1's auc: 0.850324\n",
      "Early stopping, best iteration is:\n",
      "[2964]\ttraining's auc: 0.978818\tvalid_1's auc: 0.851126\n",
      "fold n°3\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.879752\tvalid_1's auc: 0.83042\n",
      "[800]\ttraining's auc: 0.906826\tvalid_1's auc: 0.84186\n",
      "[1200]\ttraining's auc: 0.928181\tvalid_1's auc: 0.847248\n",
      "[1600]\ttraining's auc: 0.94465\tvalid_1's auc: 0.849037\n",
      "[2000]\ttraining's auc: 0.957931\tvalid_1's auc: 0.850427\n",
      "[2400]\ttraining's auc: 0.967983\tvalid_1's auc: 0.850651\n",
      "[2800]\ttraining's auc: 0.976136\tvalid_1's auc: 0.850808\n",
      "Early stopping, best iteration is:\n",
      "[2249]\ttraining's auc: 0.964496\tvalid_1's auc: 0.851066\n",
      "fold n°4\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.879926\tvalid_1's auc: 0.823037\n",
      "[800]\ttraining's auc: 0.907812\tvalid_1's auc: 0.834318\n",
      "[1200]\ttraining's auc: 0.928726\tvalid_1's auc: 0.840527\n",
      "[1600]\ttraining's auc: 0.945\tvalid_1's auc: 0.844259\n",
      "[2000]\ttraining's auc: 0.958119\tvalid_1's auc: 0.846122\n",
      "[2400]\ttraining's auc: 0.968223\tvalid_1's auc: 0.846833\n",
      "[2800]\ttraining's auc: 0.976242\tvalid_1's auc: 0.847294\n",
      "[3200]\ttraining's auc: 0.982352\tvalid_1's auc: 0.847475\n",
      "[3600]\ttraining's auc: 0.98708\tvalid_1's auc: 0.846899\n",
      "[4000]\ttraining's auc: 0.990594\tvalid_1's auc: 0.846348\n",
      "Early stopping, best iteration is:\n",
      "[3229]\ttraining's auc: 0.982735\tvalid_1's auc: 0.847635\n",
      "fold n°5\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.880554\tvalid_1's auc: 0.79495\n",
      "[800]\ttraining's auc: 0.908672\tvalid_1's auc: 0.810548\n",
      "[1200]\ttraining's auc: 0.929216\tvalid_1's auc: 0.817261\n",
      "[1600]\ttraining's auc: 0.945966\tvalid_1's auc: 0.82179\n",
      "[2000]\ttraining's auc: 0.959067\tvalid_1's auc: 0.824564\n",
      "[2400]\ttraining's auc: 0.9693\tvalid_1's auc: 0.825662\n",
      "[2800]\ttraining's auc: 0.977241\tvalid_1's auc: 0.826164\n",
      "[3200]\ttraining's auc: 0.983156\tvalid_1's auc: 0.825289\n",
      "Early stopping, best iteration is:\n",
      "[2639]\ttraining's auc: 0.974349\tvalid_1's auc: 0.826276\n",
      "fold n°6\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.880186\tvalid_1's auc: 0.833278\n",
      "[800]\ttraining's auc: 0.908946\tvalid_1's auc: 0.839752\n",
      "[1200]\ttraining's auc: 0.930055\tvalid_1's auc: 0.842629\n",
      "[1600]\ttraining's auc: 0.946281\tvalid_1's auc: 0.84357\n",
      "[2000]\ttraining's auc: 0.959091\tvalid_1's auc: 0.843318\n",
      "[2400]\ttraining's auc: 0.969191\tvalid_1's auc: 0.843653\n",
      "[2800]\ttraining's auc: 0.977153\tvalid_1's auc: 0.844035\n",
      "[3200]\ttraining's auc: 0.983163\tvalid_1's auc: 0.843762\n",
      "Early stopping, best iteration is:\n",
      "[2642]\ttraining's auc: 0.974221\tvalid_1's auc: 0.844172\n",
      "fold n°7\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.881009\tvalid_1's auc: 0.814474\n",
      "[800]\ttraining's auc: 0.909397\tvalid_1's auc: 0.826895\n",
      "[1200]\ttraining's auc: 0.929685\tvalid_1's auc: 0.833465\n",
      "[1600]\ttraining's auc: 0.94564\tvalid_1's auc: 0.837065\n",
      "[2000]\ttraining's auc: 0.958704\tvalid_1's auc: 0.839294\n",
      "[2400]\ttraining's auc: 0.968891\tvalid_1's auc: 0.841976\n",
      "[2800]\ttraining's auc: 0.976773\tvalid_1's auc: 0.843404\n",
      "[3200]\ttraining's auc: 0.982746\tvalid_1's auc: 0.844205\n",
      "[3600]\ttraining's auc: 0.987332\tvalid_1's auc: 0.845057\n",
      "[4000]\ttraining's auc: 0.990795\tvalid_1's auc: 0.845475\n",
      "[4400]\ttraining's auc: 0.993352\tvalid_1's auc: 0.845757\n",
      "[4800]\ttraining's auc: 0.995178\tvalid_1's auc: 0.845786\n",
      "[5200]\ttraining's auc: 0.996524\tvalid_1's auc: 0.845627\n",
      "[5600]\ttraining's auc: 0.997465\tvalid_1's auc: 0.845276\n",
      "Early stopping, best iteration is:\n",
      "[4813]\ttraining's auc: 0.99523\tvalid_1's auc: 0.845858\n",
      "fold n°8\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.881749\tvalid_1's auc: 0.834473\n",
      "[800]\ttraining's auc: 0.908031\tvalid_1's auc: 0.848168\n",
      "[1200]\ttraining's auc: 0.929601\tvalid_1's auc: 0.855904\n",
      "[1600]\ttraining's auc: 0.946342\tvalid_1's auc: 0.85944\n",
      "[2000]\ttraining's auc: 0.959366\tvalid_1's auc: 0.861053\n",
      "[2400]\ttraining's auc: 0.969583\tvalid_1's auc: 0.861621\n",
      "[2800]\ttraining's auc: 0.977485\tvalid_1's auc: 0.861574\n",
      "[3200]\ttraining's auc: 0.98347\tvalid_1's auc: 0.861396\n",
      "Early stopping, best iteration is:\n",
      "[2611]\ttraining's auc: 0.974032\tvalid_1's auc: 0.861802\n",
      "fold n°9\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.881941\tvalid_1's auc: 0.802691\n",
      "[800]\ttraining's auc: 0.910921\tvalid_1's auc: 0.814312\n",
      "[1200]\ttraining's auc: 0.931786\tvalid_1's auc: 0.821299\n",
      "[1600]\ttraining's auc: 0.94849\tvalid_1's auc: 0.825683\n",
      "[2000]\ttraining's auc: 0.961144\tvalid_1's auc: 0.82788\n",
      "[2400]\ttraining's auc: 0.971153\tvalid_1's auc: 0.828934\n",
      "[2800]\ttraining's auc: 0.978748\tvalid_1's auc: 0.829574\n",
      "[3200]\ttraining's auc: 0.984694\tvalid_1's auc: 0.829505\n",
      "[3600]\ttraining's auc: 0.989105\tvalid_1's auc: 0.828644\n",
      "Early stopping, best iteration is:\n",
      "[3142]\ttraining's auc: 0.983964\tvalid_1's auc: 0.82975\n",
      "CV score: 0.84558 \n"
     ]
    }
   ],
   "source": [
    "max_iter = 10\n",
    "folds = StratifiedKFold(n_splits=max_iter, random_state=1991)\n",
    "oof = np.zeros(len(comp_train))\n",
    "categorical_columns = None\n",
    "predictions_test = np.zeros(len(comp_test))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(comp_train.values, target)):\n",
    "    print(\"fold n°{}\".format(fold_))\n",
    "    X_trn, y_trn = comp_train.iloc[trn_idx][features], target.iloc[trn_idx]\n",
    "    X_val, y_val = comp_train.iloc[val_idx][features], target.iloc[val_idx]\n",
    "    X_comp_test = comp_test[features]\n",
    "    trn_data = lgb.Dataset(X_trn, y_trn, categorical_feature = categorical_columns)\n",
    "    val_data = lgb.Dataset(X_val, y_val, categorical_feature = categorical_columns)\n",
    "    num_round = 500000\n",
    "    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=400, early_stopping_rounds = 800)\n",
    "    oof[val_idx] = clf.predict(X_val, num_iteration=clf.best_iteration)\n",
    "    \n",
    "    del trn_data, val_data, X_trn, X_val, y_trn, y_val\n",
    "    gc.collect()\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = features\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance(importance_type='gain')\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "\n",
    "    current_comp_test_pred = clf.predict(X_comp_test, num_iteration=clf.best_iteration)\n",
    "    predictions_test += current_comp_test_pred / folds.n_splits\n",
    "    \n",
    "    del X_comp_test, current_comp_test_pred, clf\n",
    "    \n",
    "    gc.collect()\n",
    "  \n",
    "print(\"CV score: {:<8.5f}\".format(roc_auc_score(target, oof)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f72e10be5c0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(predictions_test).plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7302b1ae80>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi = feature_importance_df.groupby('feature')['importance'].mean().sort_values()\n",
    "plt.figure(figsize=(10,10))\n",
    "fi[-20:].plot.barh(fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oof_df = pd.DataFrame()\n",
    "oof_df['patient_id'] = comp_train['patient_id']\n",
    "oof_df[TARGET_COL] = oof\n",
    "oof_df.to_csv(OUTPUT_PATH + 'oof_lgbm_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>outcome_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>patient_2</td>\n",
       "      <td>0.190202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>patient_3</td>\n",
       "      <td>0.154327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>patient_5</td>\n",
       "      <td>0.725220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>patient_8</td>\n",
       "      <td>0.012547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>patient_14</td>\n",
       "      <td>0.403064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>patient_15</td>\n",
       "      <td>0.056895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>patient_16</td>\n",
       "      <td>0.021564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>patient_33</td>\n",
       "      <td>0.169340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>patient_38</td>\n",
       "      <td>0.043568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>patient_41</td>\n",
       "      <td>0.235209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  outcome_flag\n",
       "0   patient_2      0.190202\n",
       "1   patient_3      0.154327\n",
       "2   patient_5      0.725220\n",
       "3   patient_8      0.012547\n",
       "4  patient_14      0.403064\n",
       "5  patient_15      0.056895\n",
       "6  patient_16      0.021564\n",
       "7  patient_33      0.169340\n",
       "8  patient_38      0.043568\n",
       "9  patient_41      0.235209"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = pd.DataFrame()\n",
    "sub_df[ID_COL] = comp_test[ID_COL]\n",
    "sub_df[TARGET_COL] = predictions_test\n",
    "sub_df[[TARGET_COL]].to_excel(OUTPUT_PATH + 'preds_lgbm_3.xlsx', index=False)\n",
    "sub_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
